Biphasic Optical Signal of an oscillating nonstationary Belousov-zhabotinsky Bulk Reaction and its Similarity to Some neurophysiological Events. - Kerstin Dolzmann; Sebastian Kaltenbach; Wolfgang Hanke; Vera Maura Fernandes de Lima (Int. J. Bifurc. Chaos, 2007)

Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systems. - Sebastian Kaltenbach; Phaedon-Stelios Koutsourelakis (J. Comput. Phys., 2020)

Physics-aware, probabilistic model order reduction with guaranteed stability. - Sebastian Kaltenbach; Phaedon-Stelios Koutsourelakis (ICLR, 2021)

Physics-enhanced Neural Networks in the Small Data Regime. - Jonas Eichelsdörfer; Sebastian Kaltenbach; Phaedon-Stelios Koutsourelakis (CoRR, 2021)

Semi-supervised Invertible DeepONets for Bayesian Inverse Problems. - Sebastian Kaltenbach; Paris Perdikaris; Phaedon-Stelios Koutsourelakis (CoRR, 2022)

Interpretable reduced-order modeling with time-scale separation. - Sebastian Kaltenbach; Phaedon-Stelios Koutsourelakis; Petros Koumoutsakos (CoRR, 2023)

Interpretable learning of effective dynamics for multiscale systems. - Emmanuel Menier; Sebastian Kaltenbach; Mouadh Yagoubi; Marc Schoenauer; Petros Koumoutsakos (CoRR, 2023)

Physics-aware, probabilistic machine learning in the Small Data regime. - Sebastian Kaltenbach (2023)

Closure Discovery for Coarse-Grained Partial Differential Equations using Multi-Agent Reinforcement Learning. - Jan-Philipp von Bassewitz; Sebastian Kaltenbach; Petros Koumoutsakos (CoRR, 2024)

Generative Learning for Forecasting the Dynamics of Complex Systems. - Han Gao; Sebastian Kaltenbach; Petros Koumoutsakos (CoRR, 2024)

Generative Learning of the Solution of Parametric Partial Differential Equations Using Guided Diffusion Models and Virtual Observations. - Han Gao; Sebastian Kaltenbach; Petros Koumoutsakos (CoRR, 2024)

Closure Discovery for Coarse-Grained Partial Differential Equations Using Grid-based Reinforcement Learning. - Jan-Philipp von Bassewitz; Sebastian Kaltenbach; Petros Koumoutsakos (CPAL, 2025)

Learning Effective Dynamics across Spatio-Temporal Scales of Complex Flows. - Han Gao; Sebastian Kaltenbach; Petros Koumoutsakos (CPAL, 2025)

A Learnable Prior Improves Inverse Tumor Growth Modeling. - Jonas Weidner; Ivan Ezhov; Michal Balcerak; Marie-Christin Metz; Sergey Litvinov; Sebastian Kaltenbach; Leonhard F. Feiner; Laurin Lux; Florian Kofler; Jana Lipková; Jonas Latz; Daniel Rueckert; et al. (IEEE Trans. Medical Imaging, 2025)

Energy Matching: Unifying Flow Matching and Energy-Based Models for Generative Modeling. - Michal Balcerak; Tamaz Amiranashvili; Suprosanna Shit; Antonio Terpin; Sebastian Kaltenbach; Petros Koumoutsakos; Bjoern H. Menze (CoRR, 2025)

Optimal Lattice Boltzmann Closures through Multi-Agent Reinforcement Learning. - Paul Fischer; Sebastian Kaltenbach; Sergey Litvinov; Sauro Succi; Petros Koumoutsakos (CoRR, 2025)

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data. - Lothar Heimbach; Sebastian Kaltenbach; Petr Karnakov; Francis J. Alexander; Petros Koumoutsakos (CoRR, 2025)

Data-Driven Discovery of Interpretable Kalman Filter Variants through Large Language Models and Genetic Programming. - Vasileios Saketos; Sebastian Kaltenbach; Sergey Litvinov; Petros Koumoutsakos (CoRR, 2025)